High-quality resources:
Maybe:
HN comment and discussion: "What is a good book on statistics that one can use for self-learning?"
From that discussion:
Depends where you are starting from and what you want to learn. The linked book is a first year introduction, and does a good job of that. If you want to go further there are many other options:
Statistical Inference by Casella and Berger. This book has a very good reputation for building statistics from first principles. I won't link to them, but you can find full PDF scans online with a simple search. Amazon reviews: https://www.amazon.com/Statistical-Inference-Roger-Berger/dp…
Statistics by Freedman, Pisani, and Purves has similarly very good reviews and can be easily found online. Amazon reviews: https://www.amazon.com/Statistics-Fourth-David-Freedman-eboo…
The majority of the Berkeley data science core curriculum books are online. This is not purely statistics but 1) is taught in a modern style that makes use of computation and randomization and 2) uses tools that may be useful to learn about.
https://inferentialthinking.com/chapters/intro.html (Data 8)
https://learningds.org/intro.html (Data 100)
https://data102.org/fa23/resources/#textbooks-from-previous-… (Data 102; this gets into machine learning and pure statistics)
The Berkeley curriculum is not the only one; there are tens, possibly hundreds, of online courses. The Berkeley curriculum is just 1) quite extensive and 2) the one I happened to read the most about when I was recently researching how data science is currently taught.
The books mentioned from Berkeley's data science curriculum (they also use Python instead of R):
Computational and Inferential Thinking: The Foundations of Data Science (Data 8)
Learning Data Science (Data 100)
Probability for Data Science (Data 140)
Data 102 Textbook (Data 102) - gets into machine learning and pure statistics